Enhancing Academic Leadership Among School Administrators: Development of a Program for Schools Under Provincial Administration Organizations
Bibliographic record
Abstract
This research aimed to identify, develop, and validate the components and indicators of an academic leadership enhancement program for school administrators in institutions under Provincial Administration Organizations, using a mixed-method approach. The study employed document analysis, examining principles, concepts, and theories from domestic and international literature, textbooks, and related studies from both Thai and international researchers. The research process consisted of 1) developing and refining components and indicators using qualitative content analysis, 2) validating content validity using the Index of Item-Objective Congruence (IOC), 3) assessing the appropriateness of components and indicators using a 5-point rating scale, and 4) analyzing data using descriptive statistics and calculating Cronbach’s alpha coefficients. Seven experts validated the findings. These key informants were selected based on specific criteria, including: 1) holding a doctoral degree in educational administration, 2) having at least 5 years of experience in educational administration in higher education institutions, 3) having at least 5 years of experience in educational administration in basic education institutions or related agencies, and 4) having published research or academic articles related to academic leadership. Experts met at least two of these four criteria. The findings revealed that the program comprises five key components: 1) Commitment establishment, 2) Teaching and learning management development, 3) Curriculum development, 4) Academic atmosphere promotion, and 5) Teacher development. All components and indicators were rated as most appropriate, with mean scores ranging from 4.52 to 4.90 out of 5.00. The components of teaching and learning management development and teacher development received the highest ratings. All components demonstrated good to excellent internal consistency, with Cronbach's alpha values ranging from 0.70 to 0.92. This research presents a comprehensive and validated framework for an academic leadership enhancement program, which can be further utilized in developing school administrators under Provincial Administration Organizations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".